Social networking services provide a rich source of data for real-time event detection, particularly with the increased popularity of posting geotagged content from mobile devices in real time. However, there are several problems posed by using social networking service content to detect events in real-time. First, the sheer volume and frequency of content generated across each social networking service is immense-attempting to analyze all the content generated across multiple social networking services in real time poses considerable processing and modeling challenges. Second, the social networking service content is typically a mix of content with different focuses, ranging from content that is relevant to an event (e.g., an image of the event focus) to content that is irrelevant to an event (e.g., content that is only relevant to the user or the personal connections of the user), distributed across time and space. The volume of secondary content (e.g., content that is not about the event) tends to eclipse the primary content (e.g., content that is about the event), rendering detection of the beginning of an event difficult. Third, curation of the content relevant to the event poses an issue as well, as the event-associated content can range from content about the event focus to content about spectators of the event. For example, content about the event focus can be relevant to users interested about the event, while content about the event spectators tends to be irrelevant to the users and dilutes the value of an event feed that is generated from the event-associated content. Conversely, some entities can be interested in only the content generated by spectators of the event (e.g., a sporting event or music event), while content about the event itself is irrelevant to the entity and dilutes the value of the content feed generated from the event- or geographic region-associated content. The substantially real-time event detection can subsequently be used to notify users, used as a trigger event for trading models or trading triggers in financial market applications, or used in any other suitable manner.
Thus, there is a need in the social networking services field to create a new and useful system and method for automatic, real-time event detection based on content generated on social networking systems.